Data Science (DS) aims to extract knowledge from large amounts of data. Organizations can use the retrieved insights to achieve various performance improvements. However, DS projects often fail to fulfill their objectives due to the explorative nature of this discipline and technical as well as managerial challenges. Consequently, new approaches to support DS project execution are sought after. A viable contribution in this regard is improving knowledge retention in DS to predict socio-technical obstacles of an undertaking and derive best practices. Therefore, in this work, a template for describing the central characteristics of DS projects is proposed using a Design Science Research approach. The artifact is structured based on the common DS project stages and features 32 fields, enabling comparability and transparency in DS. The applicability of the template is demonstrated based on three DS use cases from the literature. While further steps for evaluation are pending, the template can serve as a foundation for developing a categorization model for DS projects in the future.

As data continues to grow exponentially, the fields of data management and analytics must evolve to ensure efficient data ingestion, knowledge extraction, and scalability. The Data Lakehouse architecture, which combines the best features of Data Warehouses and Data Lakes, has emerged as a potential solution. However, to fully leverage the capabilities of Data Lakehouses for time series data, it is crucial to understand the unique challenges and opportunities they present. This literature review examines proposed Data Lakehouse architectures specifically for time series data, exploring their implementation, the software technologies used, and potential real-world applications. The focus is on comparing these architectures to identify the most suitable technologies for similar implementations. Through an in-depth analysis, this study emphasizes the importance of optimizing configurations to enhance system performance and scalability, particularly for data analysis and artificial intelligence (AI) workloads.

Data Science (DS) has gained increased relevance due to the potential to extract useful insights from data. Quite commonly, this involves the utilization of Machine Learning (ML). The challenging pursuit of developing and productionizing ML models can be supported and automated through MLOps, a specialization of the DevOps paradigm from software development. Therefore, MLOps offers significant potential for DS projects, which are suffering from notable failure rates. Accordingly, this literature review focuses on examining the current state-of-the-art of the publications in this area. Most importantly, the analysis showed that the current MLOps approaches in the literature predominantly emphasize model development and deployment, while organizational aspects (business understanding, evaluation) in a DS project are neglected. As DS project success is not exclusively dependent on technical matters, advancing the MLOps field by bridging the gap between business objectives and the modeling perspective through appropriate frameworks should be pursued in future research.